Traffic signal mapping and detection

ABSTRACT

A system and method provides maps identifying the 3D location of traffic lights. The position, location, and orientation of a traffic light may be automatically extrapolated from two or more images. The maps may then be used to assist robotic vehicles or human drivers to identify the location and status of a traffic signal.

CROSS-REFERENCE TO RELATED APPLICATION

The present application is a continuation of U.S. patent applicationSer. No. 12/819,575, filed on Jun. 21, 2010, which claims the benefit ofthe filing date of U.S. Provisional Application No. 61/297,468, filedJan. 22, 2010, the disclosure of which is hereby incorporated byreference.

BACKGROUND OF THE INVENTION

1. Field of the Invention

The invention relates generally to building maps of traffic signals.More specifically, these maps may be used to perform real time trafficsignal detection.

2. Description of Related Art

A key component of a robot vehicle is the perception system, whichallows the vehicle to perceive and interpret its surroundings whiledriving. Humans have engineered the driving problem to make it easier.For example, lanes are delineated by lines painted on the road, trafficlights for precedence at intersections, brake lights, and turn signals,are all intended to simplify the perception task. Robots can use thesedriving aids, but in many cases they are able to use alternative sensingmodalities, such as radar or lidar, instead of vision. In addition tothese other sensing modalities, robots can often leverage prior maps tosimplify online perception. Using a prior map that includes stop signs,speed limits, lanes, etc., a robot vehicle can largely simplify itsonboard perception requirements to the problem of estimating itsposition with respect to the map (localization), and dealing withdynamic obstacles, such as other vehicles (perception).

Traffic signals are a major challenge for robot vehicles. Efforts havebeen made to broadcast traffic light state over radio, but this requiresa significant investment in infrastructure. While robots can oftenemploy active sensors such as radar and lidar to perceive theirsurroundings, the state of traffic signals can only be perceivedvisually. Although any vision task may be challenging due to the varietyof outdoor conditions, traffic lights have been engineered to be highlyvisible.

BRIEF SUMMARY OF THE INVENTION

The invention relates generally to building maps of traffic lights. Morespecifically, these maps may be used to perform real time detection ofthe status of traffic signals.

One aspect of the invention provides a method of determiningthree-dimensional locations of traffic signals. The method includesreceiving a plurality of images, each image of the plurality of imagesis associated with geographic location and orientation information;selecting, by a computer, one or more images of the plurality of imageswhich are associated with the geographic locations proximate to trafficintersections; for each selected image, identifying by the computer red,yellow, and green objects within the selected image; identifyingassociated ones of the red, yellow, and green objects within two or moreof the selected images, based on the geographic location and orientationinformation of the two or more selected images; determining thethree-dimensional locations of traffic signals based on (1) theidentified associations between the two or more of the selected imagesand (2) the geographic location and orientation information of the twoor more selected images; and storing the three-dimensional locations ofthe traffic signals in memory accessible by the computer.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the method includes generating a mapincluding the three-dimensional locations of the traffic signals.

In another example, each of the plurality of images is collected by oneor more cameras, and each camera is associated with a vehicle. In analternative, each of the one or more cameras is mounted on the vehicle.In another alternative, the geographic location and orientationinformation associated with each image are generated based on thegeographic location and orientation information of the camera asdetermined by a geographic position device.

In another example, the geographic location and orientation informationassociated with each image are determined by a laser positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by a GPS positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by an inertial positioningdevice.

In another example, the geographic location information is GPS latitudeand longitude coordinates.

In another example, where each of the plurality of images is collectedby one or more cameras, and each camera is associated with a vehicle,each of the one or more cameras is associated with gain and shutterspeed which are set to avoid saturation of a traffic signal light.

In another example, where each of the plurality of images is collectedby one or more cameras, and each camera is associated with a vehicle,each image of the plurality of images is uploaded from each of the oneor more cameras to the computer via a network.

In another example, where each of the plurality of images is collectedby one or more cameras, and each camera is associated with a vehicle,each of the one or more cameras is positioned to minimally obstruct theview of a driver of the vehicle.

In another example, the identified red, yellow, and green objects arethe appropriate size and aspect ratios to correspond to traffic signals.In still a further example, the identifying associated ones of the red,yellow, and green objects of two or more selected images is based on anassociation distance between identified objects of the two or moreselected images.

In another example, the identifying associated ones of the red, yellow,and green objects of two or more selected images is based on thephysical dimensions of a traffic signal.

In another example, the identifying associated red, yellow, and greenobjects of two or more selected images is based on direct motioncompensation between the selected images, wherein each selected image istaken by a camera mounted on a moving vehicle.

In another example, the method includes identifying the identified onesof red, yellow, and green objects within a selected image as an objectother than a traffic signal light based on the direct motioncompensation.

In another example, the method includes determining a lane associatedwith the particular traffic signal based on comparing the determined thethree-dimensional location of the particular traffic signal to a map oflanes through an intersection.

In another example, where the method includes generating a map includingthe three-dimensional locations of the traffic signals, the methodincludes downloading the map to a second computer of a vehicle.

In another example, where the method includes generating a map includingthe three-dimensional locations of the traffic signals, the methodincludes downloading the map to a client device.

In another example, where the method includes generating a map includingthe three-dimensional locations of the traffic signals, the methodincludes receiving a geographic location from a client device andtransmitting to the client device a portion of the map based on thereceived geographic location.

Another aspect of the invention provides a device for determiningthree-dimensional locations of traffic signals. The device includes aprocessor and memory. The processor is configured to receive a pluralityof images, each image of the plurality of images is associated withgeographic location and orientation information; select one or moreimages of the plurality of images which are associated with geographiclocations proximate to traffic intersections; for each selected image,identify red, yellow, and green objects within the selected image;identify associated ones of the red, yellow, and green objects withintwo or more of the selected images, based on the geographic location andorientation information of the two or more selected images; determinethe three-dimensional locations of traffic signals based on (1) theidentified associations between the two or more of the selected imagesand (2) the geographic location and orientation information of the twoor more selected images; and store the three-dimensional locations oftraffic signals in memory accessible by the device.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the processor is configured to generatea map including the three-dimensional locations of traffic signals.

In another example, each of the plurality of images is collected by oneor more cameras, each camera associated with a vehicle. In analternative, each of the one or more cameras is mounted on the vehicle.In another alternative, the geographic location and orientationinformation associated with each image are generated based on thegeographic location and orientation of the camera as determined by ageographic position device.

In another example, the geographic location and orientation informationassociated with each image are determined by a laser positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by a GPS positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by an inertial positioningdevice.

In another example, the geographic location information is GPS latitudeand longitude coordinates.

In another example, where each of the plurality of images is collectedby one or more cameras, each of the one or more cameras is associatedwith gain and shutter speed which are set to avoid saturation of atraffic signal light.

In another example, where each of the plurality of images is collectedby one or more cameras, each image of the plurality of images isuploaded from each of the one or more cameras to the device via anetwork.

In another example, where each of the plurality of images is collectedby one or more cameras, each of the one or more cameras is positioned tominimally obstruct the view of a driver of the vehicle.

In another example, the identified red, yellow, and green objects arethe appropriate size and aspect ratios to correspond to traffic signals.

In another example, the identifying associated ones of the red, yellow,and green objects of two or more selected images is based on anassociation distance between identified objects of the two or moreselected images.

In another example, the identifying associated ones of the red, yellow,and green objects of two or more selected images is based on thephysical dimensions of a traffic signal.

In another example, the identifying associated red, yellow, and greenobjects of two or more selected images is based on direct motioncompensation between the selected images, wherein each selected image istaken by a camera mounted on a moving vehicle.

In another example, the processor is further configured to identify theidentified ones of red, yellow, and green objects within a selectedimage as an object other than a traffic signal light based on the directmotion compensation.

In another example, the processor is further configured to determine alane associated with the particular traffic signal based on comparingthe determined the three-dimensional location of the particular trafficsignal to a map of lanes through an intersection.

In another example, the processor is further configured to download themap to a second device associated with a vehicle.

In another example, the processor is further configured to download themap to a client device.

In another example, where the processor is configured to generate a mapincluding the three-dimensional locations of traffic signals, theprocessor is further configured to receive a geographic location from aclient device and transmitting to the client device a portion of the mapbased on the received geographic location.

An additional aspect of the invention provides a method of determiningthe status of a traffic signal. The method includes repeatedlydetermining a current location of a client device; determining anestimated location of the boundaries of a traffic signal based on acomparison of the current location of the client device to a map ofthree-dimensional locations of traffic signals; collecting images of theestimated location; for each collected image, identifying by the clientdevice red, yellow, and green objects within the boundaries of theestimated location of the traffic signal; and determining the status ofthe traffic signal based on the color of the identified object.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the images are collected by a camera ofthe client device.

In another example, the current location of the client device isdetermined by a laser based positioning device. In another example, thecurrent location of the client device is determined by a laserpositioning device.

In another example, the current location of the client device isdetermined by a GPS positioning device.

In another example, the current location of the client device isdetermined by an inertial positioning device.

In another example, where the images are collected by a camera of theclient device, the camera is associated with gain and shutter speedwhich are set to avoid saturation of a traffic signal light.

In another example, where the images are collected by a camera of theclient device, the method includes accessing the map ofthree-dimensional locations of traffic signals from memory of the clientdevice.

In another example, the identified red, yellow, and green objects arethe appropriate size and aspect ratios to correspond to traffic signals.

In another example, determining the status of the traffic signal isbased on the location of the identified object within the boundaries ofthe estimated location.

In another example, the boundaries of the estimated location areassociated with dimensions, and the dimensions are greater than thedimensions of the traffic signal.

In another example, the method includes determining a traffic laneassociated with the client device based on comparing the currentlocation of the client device to a map of traffic lanes through anintersection.

In one alternative, determining an estimated location of the boundariesof the traffic signal is based on the traffic lane associated with theclient device.

In another example, the method includes comprising requesting the map ofthree-dimensional locations of traffic signals from a computer over anetwork, the request including the current location of the clientdevice.

In another example, the method includes receiving the map ofthree-dimensional locations of traffic signals from a computer over anetwork.

In another example, the method includes determining whether the statusof the traffic signal has changed from a default status. In onealternative, if there are no identified objects within the boundaries ofthe estimated location of the traffic signal, the method includesdetermining that the status of the traffic signal is the default status.In an additional alternative, the default status is a yellow light. In afurther alternative, the default status is a red light.

In another example, the method includes transmitting the status of thetraffic signal to a computer associated with a vehicle.

In another example, the method includes decelerating the vehicle if thestatus of the light is red or yellow. In another example, the methodincludes identifying the status of the traffic signal audibly.

In another example, the method includes providing driving instructionsbased on the status of the traffic signal.

In another example, the client device includes an electronic display andthe method further comprising identifying the status of the trafficsignal on the electronic display.

Another aspect of the invention provides a device for determiningthree-dimensional locations of traffic signals. The device includes aprocessor and memory, including a first part for storing secondaryresource files. The processor is configured to repeatedly determine thecurrent location of a client device; determine an estimated location ofthe boundaries of a traffic signal based on a comparison of the currentlocation of the client device to a map of three-dimensional locations oftraffic signals; collect images of the estimated location; for eachcollected image, identify by the client device red, yellow, and greenobjects within the boundaries of the estimated location of the trafficsignal; and determine the status of the traffic signal based on thecolor of the identified object.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the device includes a camera forcollecting the images of the estimated location.

In another example, the location of the client device is determined by alaser positioning device.

In another example, the device includes a laser based positioning deviceand wherein the current location of the client device is determined by alaser positioning device.

In another example, the device includes a GPS based positioning deviceand wherein the current location of the client device is determined bythe GPS positioning device.

In another example, the device includes an inertial based positioningdevice and wherein the current location of the client device isdetermined by the inertial positioning device.

In another example, the camera is associated with gain and shutter speedwhich are set to avoid saturation of a traffic signal light.

In another example, the processor is configured to access the map ofthree-dimensional locations of traffic signals from the memory of theclient device.

In another example, the identified red, yellow, and green objects arethe appropriate size and aspect ratios to correspond to traffic signals.

In another example, the processor determines the status of the trafficsignal based on the location of the identified object within theboundaries of the estimated location.

In another example, the boundaries of the estimated location areassociated with dimensions, and the dimensions are greater than thedimensions of the traffic signal.

In another example, the processor is further configured to determine atraffic lane associated with the client device based on comparing thecurrent location of the client device to a map of traffic lanes throughan intersection.

In another example, the processor determines an estimated location ofthe boundaries of the traffic signal based on the traffic laneassociated with the client device.

In another example, the processor is further configured to request themap of three-dimensional locations of traffic signals from a computerover a network, the request including the current location of the clientdevice.

In another example, the processor determines an estimated location ofthe boundaries of the traffic signal the processor is further configuredto receive the map of three-dimensional locations of traffic signalsfrom a computer over a network.

In another example, the processor is further configured to determinewhether the status of the traffic signal has changed from a defaultstatus. In an alternative, the processor is further configured todetermine that the status of the traffic signal is the default status ifthere are no identified objects within the boundaries of the estimatedlocation of the traffic signal. In one alternative, the default statusis a yellow light. In another alternative, the default status is a redlight.

In another example, the processor is further configured to transmit thestatus of the traffic signal to a computer associated with a vehicle.

In another example, the processor is further configured to deceleratethe vehicle if the status of the light is red or yellow. In anotherexample, the processor is further configured to identify the status ofthe traffic signal audibly.

In another example, the processor is further configured to providedriving instructions based on the status of the traffic signal.

In another example, the device is mounted to a vehicle.

In another example, the device is a portable device. In another example,the device includes an electronic display and the processor is furtherconfigured to identify the status of the traffic signal on theelectronic display.

A further aspect of the invention provides a method of determining thestatus of a traffic signal. The method includes repeatedly determining acurrent location of a client device; determining an estimated locationof the boundaries of a traffic signal based on a comparison of thecurrent location of the client device to a map of three-dimensionallocations of traffic signals; collecting images of the estimatedlocation; for each collected image, identifying by the client devicered, yellow, and green objects within the boundaries of the estimatedlocation of the traffic signal; and determining the status of thetraffic signal based on the color of the identified object.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the positioning device is a laserpositioning device.

In another example, the positioning device is a GPS positioning device.

In another example, the positioning device is an inertial positioningdevice.

In another example, the method includes positioning the camera on thevehicle to face directly ahead of the vehicle.

In another example, the method includes positioning the camera to theright of the rear-view mirror.

In another example, the method includes positioning the camera to limitobstruction of a field of view of a driver.

In another example, the memory is local memory of the camera.

In another example, the geographic locations are defined as GPS latitudeand longitude coordinates.

In another example, the method includes receiving the plurality ofimages and the associated geographic location and orientationinformation; selecting one or more images of the plurality of imageswhich are associated with geographic locations proximate to trafficintersections; for each selected image, identifying red, yellow, andgreen objects within the selected image; identifying associated ones ofthe red, yellow, and green objects within two or more of the selectedimages, based on the geographic location and orientation information ofthe two or more selected images; determining the three-dimensionallocations of traffic signals based on (1) the identified associationsbetween the two or more of the selected images and (2) the geographiclocation and orientation information of the two or more selected images;and storing the three-dimensional locations of traffic signals in memoryaccessible by the computer. In an alternative, the method includesgenerating a map including the three-dimensional locations of trafficsignals.

In another example, where the method includes identifying associatedones of the red, yellow, and green objects within two or more of theselected images the identified red, yellow, and green objects are theappropriate size and aspect ratios to correspond to traffic signals.

In another example, where the method includes identifying associatedones of the red, yellow, and green objects within two or more of theselected images, the identifying associated ones of the red, yellow, andgreen objects of two or more selected images is based on an associationdistance between identified objects of the two or more selected images.

In another example, where the method includes identifying associatedones of the red, yellow, and green objects within two or more of theselected images, the identifying associated ones of the red, yellow, andgreen objects of two or more selected images is based on the physicaldimensions of a traffic signal.

In another example, where the method includes identifying associatedones of the red, yellow, and green objects within two or more of theselected images, the identifying associated red, yellow, and greenobjects of two or more selected images is based on direct motioncompensation between the selected images.

In another example, where the method includes identifying red, yellow,and green objects within the selected image, the method includesidentifying the identified ones of red, yellow, and green objects withina selected image as an object other than a traffic signal light based onthe direct motion compensation.

In another example, where the method includes identifying red, yellow,and green objects within the selected image, the method includesdetermining a lane associated with the particular traffic signal basedon comparing the determined the three-dimensional location of theparticular traffic signal to a map of lanes through an intersection.

Yet another aspect of the invention provides a device for collectingimages of traffic signals. The device includes a vehicle; a camera withgain and shutter speed set to avoid saturation of a traffic signallight, the camera being mounted to the vehicle; a positioning device; aprocessor coupled to the vehicle; and memory, including a first part forstoring images. The processor is configured to receive images from thecamera; identify a geographic position associated with each image of thereceived images; store the images, geographic positions, andassociations in the memory; and transmit the images, geographicpositions, and associations to a computer over a network.

As discussed herein, different features may be used in any combinationin any embodiment. For example, the camera is positioned on the vehicleto face directly ahead of the vehicle. In another example, the camera ispositioned to the right of the rear-view mirror. In another example, thecamera is positioned to limit obstruction of a field of view of adriver. In another example, the memory is local memory of the camera.

Still another aspect of the invention provides a client device includinga processor and a computer. The computer includes memory and aprocessor. The processor is configured to receive a plurality of images,each image of the plurality of images is associated with geographiclocation and orientation information; select one or more images of theplurality of images which are associated with geographic locationsproximate to traffic intersections; for each selected image, identifyred, yellow, and green objects within the selected image; identifyassociated ones of the red, yellow, and green objects within two or moreof the selected images, based on the geographic location and orientationinformation of the two or more selected images; determine thethree-dimensional locations of traffic signals based on (1) theidentified associations between the two or more of the selected imagesand (2) the geographic location and orientation information of the twoor more selected images; generate a map of the three-dimensionallocations of traffic signals; receive a request, from the client device,for a portion of the map, the request identifying a geographic location;identify a relevant portion of the map based on the identifiedgeographic location; and transmit the relevant portion of the map to theclient device. The processor of the second device is configured torepeatedly determine the current location of a client device; transmit arequest including the current location of the client device; receive therelevant portion of the map; determine an estimated location of theboundaries of a traffic signal based on a comparison of the currentlocation of the client device to received relevant portion of the map;collect images of the estimated location; for each collected image,identify red, yellow, and green objects within the boundaries of theestimated location of the traffic signal; and determine the status ofthe traffic signal based on the color of the identified object withinthe boundaries of the estimated location of the traffic signal.

As discussed herein, different features may be used in any combinationin any embodiment. For example, each of the plurality of images iscollected by one or more cameras, each camera associated with a vehicle.

In another example, the geographic location and orientation informationassociated with each image of the plurality of images is generated basedon the geographic location and orientation of the camera as determinedby a geographic position device associated with the associated vehicle.

In another example, the geographic location and orientation informationassociated with each image are determined by a laser positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by a GPS positioning device.

In another example, the geographic location and orientation informationassociated with each image are determined by an inertial positioningdevice.

In another example, the geographic location information is GPS latitudeand longitude coordinates. In another example, where each of theplurality of images is collected by one or more cameras, each of the oneor more cameras is associated with gain and shutter speed which are setto avoid saturation of a traffic signal light.

In another example, where each of the plurality of images is collectedby one or more cameras, each image of the plurality of images isuploaded from each of the one or more cameras to the computer via anetwork.

In another example, where each of the plurality of images is collectedby one or more cameras, each of the one or more cameras is positioned tominimally obstruct the view of a driver of the vehicle.

In another example, the red, yellow, and green objects identified by theprocessor of the computer are the appropriate size and aspect ratios tocorrespond to traffic signals.

In another example, the identifying associated ones of the red, yellow,and green objects of two or more selected images by the processor of thecomputer is based on an association distance between identified objectsof the two or more selected images.

In another example, the identifying associated ones of the red, yellow,and green objects of two or more selected images by the processor of thecomputer is based on the physical dimensions of a traffic signal.

In another example, the identifying associated red, yellow, and greenobjects of two or more selected images by the processor of the computeris based on direct motion compensation between the selected images.

In another example, the processor of the first computer is furtherconfigured to identify the identified ones of red, yellow, and greenobjects within a selected image as an object other than a traffic signallight based on the direct motion compensation.

In another example, the processor of the first computer is furtherconfigured to determine a lane associated with the particular trafficsignal based on comparing the determined the three-dimensional locationof the particular traffic signal to a map of lanes through anintersection.

In another example, the client device includes an electronic display andthe processor of the client device is further configured to identify thestatus of the traffic signal on the electronic display.

In another example, the current location of the client device isdetermined by a laser positioning device. In another example, thecurrent location of the client device is determined by a GPS positioningdevice.

In another example, the current location of the client device isdetermined by an inertial positioning device.

In another example, the collected images are collected by a clientcamera of the client device.

In another example, the client camera is associated with gain andshutter speed which are set to avoid saturation of a traffic signallight.

In another example, the processor of the client device is furtherconfigured to store the received portion of the map in memory of theclient device and access the received portion from the memory.

In another example, the red, yellow, and green objects identified by theprocessor of the client device are the appropriate size and aspectratios to correspond to traffic signals.

In another example, determining the status of the traffic signal isbased on the location of the identified object within the boundaries ofthe estimated location.

In another example, the boundaries of the estimated location areassociated with dimensions, and the dimensions are greater than thedimensions of the traffic signal.

In another example, the client device is further configured to determinea traffic lane associated with the client device based on comparing thecurrent location of the client device to a map of traffic lanes throughan intersection. In one alternative, the processor of the client deviceis further configured to determine an estimated location of theboundaries of the traffic signal is based on the traffic lane associatedwith the client device.

In another example, the processor of the client device is furtherconfigured to determine whether the status of the traffic signal haschanged from a default status.

In another example, the processor of the client device is furtherconfigured to determine that the status of the traffic signal is thedefault status if there are no identified objects within the boundariesof the estimated location of the traffic signal. In one alternative, thedefault status is a yellow light. In another alternative, the defaultstatus is a red light.

In another example, the client device is further configured to transmitthe status of the traffic signal to a computer associated with avehicle.

In another example, the processor of the client device is furtherconfigured to transmit an instruction to decelerate the vehicle if thestatus of the light is red or yellow.

In another example, the client device includes one or more speakers andthe processor of the client device is further configured to identify thestatus of the traffic signal audibly.

In another example, the processor of the client device is furtherconfigured to provide driving instructions based on the status of thetraffic signal.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1A is a functional diagram of a system in accordance with an aspectof the invention.

FIG. 1B is a pictorial diagram of a system in accordance with an aspectof the invention.

FIG. 2 is an exemplary image of traffic signals and lights in accordancewith an aspect of the invention.

FIG. 3 is a flow diagram in accordance with an aspect of the invention.

FIG. 4 is a flow diagram in accordance with an aspect of the invention.

FIG. 5 is a diagram of a traffic intersection in accordance with anaspect of the invention.

FIG. 6 is an exemplary histogram of experimental data in accordance withan aspect of the invention.

FIG. 7 is an exemplary confusion matrix of experimental data inaccordance with an aspect of the invention.

DETAILED DESCRIPTION

Aspects, features and advantages of the invention will be appreciatedwhen considered with reference to the following description of exemplaryembodiments and accompanying figures. The same reference numbers indifferent drawings may identify the same or similar elements.Furthermore, the following description is not limiting; the scope of theinvention is defined by the appended claims and equivalents.

Cameras with fixed exposure and aperture may be directly calibrated tocollect images of traffic light color levels. The position, location,and orientation of a traffic light may be automatically extrapolatedfrom two or more of such images. This information may then be used togenerate maps which identify the 3D location of traffic lights. These 3Dmaps of traffic signals may allow a client device to anticipate andpredict traffic lights.

As shown in FIGS. 1A and 1B, a system 100 in accordance with one aspectof the invention includes a computer 110 containing a processor 120,memory 130 and other components typically present in general purposecomputers.

The memory 130 stores information accessible by processor 120, includinginstructions 132 and data 134 that may be executed or otherwise used bythe processor 120. The memory 130 may be of any type capable of storinginformation accessible by the processor, including a computer-readablemedium, or other medium that stores data that may be read with the aidof an electronic device, such as a hard-drive, memory vehicled, ROM,RAM, DVD or other optical disks, as well as other write-capable andread-only memories. Systems and methods may include differentcombinations of the foregoing, whereby different portions of theinstructions and data are stored on different types of media.

The instructions 132 may be any set of instructions to be executeddirectly (such as machine code) or indirectly (such as scripts) by theprocessor. For example, the instructions may be stored as computer codeon the computer-readable medium. In that regard, the terms“instructions” and “programs” may be used interchangeably herein. Theinstructions may be stored in object code format for direct processingby the processor, or in any other computer language including scripts orcollections of independent source code modules that are interpreted ondemand or compiled in advance. Functions, methods and routines of theinstructions are explained in more detail below.

The data 134 may be retrieved, stored or modified by processor 120 inaccordance with the instructions 132. For instance, although the systemand method is not limited by any particular data structure, the data maybe stored in computer registers, in a relational database as a tablehaving a plurality of different fields and records, XML documents orflat files. The data may also be formatted in any computer-readableformat. By further way of example only, image data may be stored asbitmaps comprised of grids of pixels that are stored in accordance withformats that are compressed or uncompressed, lossless (e.g., BMP) orlossy (e.g., JPEG), and bitmap or vector-based (e.g., SVG), as well ascomputer instructions for drawing graphics. The data may comprise anyinformation sufficient to identify the relevant information, such asnumbers, descriptive text, proprietary codes, references to data storedin other areas of the same memory or different memories (including othernetwork locations) or information that is used by a function tocalculate the relevant data.

The processor 120 may be any conventional processor, such as processorsfrom Intel Corporation or Advanced Micro Devices. Alternatively, theprocessor may be a dedicated device such as an ASIC. Although FIG. 1functionally illustrates the processor and memory as being within thesame block, it will be understood by those of ordinary skill in the artthat the processor and memory may actually comprise multiple processorsand memories that may or may not be stored within the same physicalhousing. For example, memory may be a hard drive or other storage medialocated in a server farm of a data center. Accordingly, references to aprocessor or computer will be understood to include references to acollection of processors or computers or memories that may or may notoperate in parallel.

The computer 110 may be at one node of a network 150 and capable ofdirectly and indirectly communicating with other nodes of the network.For example, computer 110 may comprise a web server that is capable ofcommunicating with client devices 170-72 via network 150 such thatserver 110 uses network 150 to transmit and display or otherwise provideinformation to a user, such as person 191 or 192 in FIG. 1B. Server 110may also comprise a plurality of computers that exchange informationwith different nodes of a network for the purpose of receiving,processing and transmitting data to the client devices. In thisinstance, the client devices will typically still be at different nodesof the network than any of the computers comprising server 110.

Network 150, and intervening nodes between server 110 and clientdevices, may comprise various configurations and use various protocolsincluding the Internet, World Wide Web, intranets, virtual privatenetworks, local Ethernet networks, private networks using communicationprotocols proprietary to one or more companies, cellular and wirelessnetworks (e.g., WiFi), instant messaging, HTTP and SMTP, and variouscombinations of the foregoing. Although only a few computers aredepicted in FIGS. 1-2, it should be appreciated that a typical systemcan include a large number of connected computers.

Each client device may be configured similarly to the server 110, with aprocessor 120, memory and instructions 132. Each client device 170-72may be a device intended for use by a person 191-192, and have all ofthe components normally used in connection with a computer such as acentral processing unit (CPU), memory (e.g., RAM and internal harddrives) storing data 162 and instructions such as a web browser, anelectronic display 164 (e.g., a monitor having a screen, a small LCDtouch-screen, a projector, a television, a computer printer or any otherelectrical device that is operable to display information), and userinput 166 (e.g., a mouse, keyboard, touch-screen and/or microphone). Theclient device may also include a camera 176, geographic positioncomponent 168, one or more speakers 174, a network interface device, aswell as all of the components used for connecting these elements to oneanother.

Although the client devices 170-72 may each comprise a full-sizedpersonal computer, they may alternatively comprise mobile devices whichmay or may not be capable of wirelessly exchanging data with a serverover a network such as the Internet. By way of example only, clientdevice 172 may be a vehicle-mounted device or connected to a vehiclesuch that client device 172 may exchange information with the vehicle'scomputer. In another example, client device 171 may be awireless-enabled PDA or a cellular phone capable of obtaininginformation via the Internet. The user may input information using asmall keyboard (in the case of a Blackberry phone), a keypad (in thecase of a typical cell phone) or a touch screen (in the case of a PDA).Indeed, client devices in accordance with the systems and methodsdescribed herein may comprise any device capable of processinginstructions and transmitting data to and from humans and othercomputers including general purpose devices, network computers lackinglocal storage capability, etc.

The client devices may also include a geographic position component todetermine the geographic location and orientation of the device. Forexample, client device 170 may include a GPS receiver to determine thedevice's latitude, longitude and/or altitude position. Other locationsystems such as laser-based localization systems, inertial-aided GPS, orcamera-based localization may also be used. In addition, the geographicposition component may also comprise software for determining theposition of the device based on other signals received at the clientdevice 171, such as signals received at a cell phone's antenna from oneor more cellular towers if the client device is a cell phone.

Client device 171 may also include other features, such as anaccelerometer, gyroscope or other acceleration device 168 to determinethe direction in which the device is oriented. By way of example only,the acceleration device may determine its pitch, yaw or roll (or changesthereto) relative to the direction of gravity or a plane perpendicularthereto. In that regard, it will be understood that a client device'sprovision of location and orientation data as set forth herein may beprovided automatically to the user, to the server, or both.

Returning to FIG. 1A, data 134 may include image data such as images oftraffic signals. The images may be associated with various informationincluding the position, location, and orientation of the vehicle orcamera at the time the image was taken. The image data may also beassociated with labels indicating the position and location of a trafficsignal in the image. These labels may be generated by various methods.For example, one method for generating labeled images is to use a teamof human labelers. However, human labelers may be relatively slow whenrequired to work on the high density of lights in a typical urbanenvironment. Furthermore, even the best human labelers are not alwaysaccurate in their placement of labels. In another example described inmore detail below, the labels may be generated automatically by usingtraffic signal classifier 140.

Image data 134 may also include associations with other images. Forexample, where two labels in two different images identify the sametraffic signal, these labels (or images) may be associated with oneanother. As will be described in more detail below, these labels andassociations between images may be used to generate traffic signal maps138 which identify estimated 3D positions of traffic signals.

These images may be collected, for example, by manually driving avehicle instrumented with cameras and navigation systems such as GPS,inertial, and/or laser systems through intersections and collectprecisely time stamped laser ranges and camera images. Vehicles, such asvehicles 180-81, may use various types of cameras 182 mounted in variousconfiguration to collect traffic light images. For example, a Point GreyGrasshopper 5 MP camera may be positioned to face straight ahead andmounted to the right of rear-view mirror, where it minimally obstructsthe driver's field of view. A specific region-of-interest may beselected for the camera, such as a 2040×1080 region, of a camera with afixed lens with a 30 degree field of view. For example, the camera maybe calibrated to be able to detect traffic signals at 150 m whentraveling at 55 MPH to ensure a reasonable breaking distance.

Vehicles 180-181 or camera 182 may also include a geographic position178 component described above. The geographic position component may beused to identify the geographic location, orientation, and position ofthe camera or vehicle when a particular image was taken.

Vehicles 180-81 may record images for use by server 110. The images andassociated information may be uploaded to server 110 via network 150from the vehicle or loaded to server 110 directly.

In order to collect the images of light traffic signals at night, thegain and shutter speeds may be set to avoid saturation of the trafficlights, particularly the bright LED-based green lights. These settingsmay be used to yield a relatively dark image even during the day.

FIG. 2 depicts an embodiment 200 which will be described in detailbelow. It will be understood that the operations do not have to beperformed in the precise order described below. Rather, various stepscan be handled in a different order or simultaneously.

As shown in block 210 of FIG. 2, image data is received by server 110.Then, at block 220, the server filters down the images to the mostrelevant ones, i.e., images likely to contain traffic lights. Generally,traffic signals will be positioned at intersections, so geo-spatialqueries may be used to discard images taken when no intersections arelikely to be visible.

After winnowing the set of images to those that were taken while thevehicle was approaching an intersection, the images are classified andlabeled, as shown in blocks 230 and 240. Server 110 may use a trafficsignal classifier which finds brightly-colored red, yellow, and greenblobs with appropriate size and aspect ratios, and these blobs are thenused as tentative traffic signal labels for the position estimationprocess. Although the present invention is described in conjunction withtypical vertical traffic signals having a set of red, yellow, and greenlights, it will be understood that this specific structure is usedmerely as an example. Traffic signals may have varied and sometimescomplex geometries and the invention may operate with any number ofthese additional geometries.

FIG. 3 depicts human labeled examples of traffic signals. For eachpositive example, eight additional negative examples may be generated.If the newly generated examples overlap with a positive example, forexample, where two traffic signals are very close together, the newlygenerated examples may be discarded.

The output of the labeling step my be a large number of labels, but withno information about which labels belong to which traffic signals. Toestimate the position of an object in 3D requires at least two labels indifferent images, and the position estimate will generally improve ifmore labels are available. However, in order to identify two or moreimages which include the same subject matter, such as a particulartraffic signal, the labels of the images must be associated with oneanother. Two images may be associated if the labels associated withthose images fall within an association distance of one another. Forexample, each label may have a diameter d. If the centers of two labelsare within a relatively small distance of one another, such as d or 10d, these labels may be associated with one another. In another example,if two labels overlap, the labels may be associated. Associations may bedetermined between labels in image sequences, between 3D objects onceposition estimation has been performed, or by using an iterativeapproach that combines both types of associations.

In order to identify the associations, the server may make someinferences about the types of traffic signals pictured. For example, thefull size of the traffic signal may be inferred by assuming that thesignal has the standard vertical red-yellow-green structure, which is byfar the most common configuration. These full-sized traffic signallabels may make associating labels that come from traffic signals thathave changed color simpler.

Label association between images may be performed in various ways. Forexample, in the case of near-affine motion and/or high frame rates,template trackers can be used to associate a label in one image with alabel in the next image. In another example, where the camera frame rateis low, such as 4 fps, and the object motion may be fully projective,direct motion compensation may be used.

As noted above, the precise camera pose, orposition/location/orientation, is known for each image. If the camera ismounted on a moving vehicle, the cumulative error in the pose may berelatively low, for example 1% of distance traveled, over time periodsof several seconds. In some examples, position estimates of the vehiclemay be refined by offline optimization methods to yield positionaccuracy within 0.15 m.

Returning to block 250 of FIG. 2, server 110 may use direct motioncompensation to identify associations between labels in differentimages. The apparent motion of objects due to changes in roll, pitch,and yaw may be straightforward to correct using the intrinsic cameramodel, but some estimate of the object's position may be necessary tocorrect the apparent motion of objects due to the vehicle's forwardmotion. The object's apparent position in the image restricts itsposition to somewhere along a ray, and a rough estimate of the distanceof the object can be made by assuming that the object is a trafficsignal of particular dimension. The distance d to an object with truewidth w and apparent width {tilde over (w)} in an image taken by acamera with focal length f_(u) is:

$d \approx \frac{\overset{\sim}{w}}{2\;{\tan\left( \frac{\overset{\sim}{w}}{2\;{fu}} \right)}}$The direction vector X=[u, v]^(T) may be computed by using the cameramodel to correct for radial distortion, and the rough 3D position of theobject is:

${y = {{\sin\left( {\arctan\left( {- u} \right)} \right)}d}},{z = {{\sin\left( {\arctan\left( {- v} \right)} \right)}d}},{x = \sqrt{d^{2} - y^{2} - {z^{2}.}}}$If T1 and T2 are the 4×4 transformation matrices for two different timesfrom the vehicle's frame to a locally smooth coordinate frame and C isthe transform from the vehicle frame to the camera frame the relativemotion of the object from one image to another may be corrected as:{circumflex over (x)} ₂ =CT ₂ T ₁ ⁻¹ C ⁻¹ x ₁

The distorted image coordinates may be computed by using the camera'sintrinsic model. As noted above, two labels (of traffic signals in twoimages) may be associated if the labels fall within an associationdistance of one another. Thus, long sequences of labels may beassociated indicating that they all are associated with images of aparticular traffic signal.

In some instances, a label may correspond to other types of objects, forexample, tail lights of another vehicle. In these cases, the roughdistance estimate and the subsequent motion compensation will beincorrect, and the likelihood of label associations between objectsincorrectly classified as traffic signals may be reduced. This may alsoallow the server to filter out spurious labels.

If the motion-corrected label overlaps another label, it is likely thatthese labels correspond to the same object. The 3D position of theobject may be estimated from a sequence of the corresponding labels.

As shown in block 260, server 110 may use the associations to determinethe 3D location of the traffic signals. Specifically, the pose of the 3Dobject may be estimated based on the associated labels in two or moreimages. For example, the optimal triangulation method may be used,however, this method may be used with at most three labels and may beless useful when there are many labels for the same traffic signal. Inanother example, the pose may be estimated by using linear triangulationand the Direct Linear Transform, a least-squares method.

Using the least squares method, each provisionally classified imagelabel may be associated with the image coordinate x. These imagecoordinates may be converted into a direction vector x using thecamera's intrinsic model to correct for radial distortion, etc.

The 3D point X may be estimated such that for each label directionvector x_(i) and the camera projection matrix P_(i),x _(i) =P _(i) xThese equations may be combined into the form:AX=0which is a linear equation in X. To eliminate the homogeneous scalefactor inherent in projective geometry, a 3n×4 matrix A may be assembledfrom the cross product of each of the image labels {x₁, x₂, . . . } fora particular object:

$A = \begin{bmatrix}{\left\lbrack x_{1} \right\rbrack \times {HP}_{1}} \\{\left\lbrack x_{2} \right\rbrack \times {HP}_{2}} \\\ldots\end{bmatrix}$where the cross-product matrix is:

$\lbrack x\rbrack_{\times} = \begin{bmatrix}0 & {- 1} & {- u} \\1 & 0 & {- v} \\u & v & 0\end{bmatrix}$and

$H = \begin{bmatrix}1 & 0 & 0 & 0 \\0 & 1 & 0 & 0 \\0 & 0 & 1 & 0\end{bmatrix}$Performing singular value decomposition on A, where A=UΣV^(T), thesolution for X is the de-homogenized singular vector that corresponds tothe smallest singular value of A, or the right most value of A.

The orientation of the signal may be estimated as the reciprocal headingof the mean vehicle heading over all the images labels used to estimatethe traffic signal position.

The information generated above may be used to generate map informationwhich describes the 3D location and geometries of traffic signals, asshown in block 270. The accuracy of the map information may be sensitiveto the extrinsic parameters of the camera, i.e., the transform thatrepresents the camera's position and orientation relative to thevehicle's coordinate frame. Assuming a reasonable initial estimate ofthe extrinsic parameters, these parameters may be calibrated byminimizing the reprojection effort of the traffic signals usingcoordinate descent:

$e^{*} = {\arg\;{\min\limits_{e}{\sum\limits_{i}\left( {{{RadialLensDistortion}\mspace{11mu}\left( {PX}_{i,e} \right)} - x_{i}} \right)^{2}}}}$

Here, X_(e) are the traffic signal positions estimated by the mappingpipeline using extrinsic parameters e. A similar process may be used toestimate the timing delay between when the image is taken by the cameraand when it is transmitted to the computer (although hardware timestampsmay also be used). This timing delay may vary depending on the cameraframe rate and Firewire bus scheduling allocation, but it also may bestable to within a few hundredths of a second for a given configuration.The camera's intrinsic parameters, which determine the lens distortion,may be calibrated for a standard radial lens model using thecheckerboard corner extraction procedure.

Traffic signals may also be identified to the actual lane to which theyapply. For example, some traffic signals may apply only to left or rightturn only lanes. This information may be represented as an associationbetween a traffic signal and the different allowed routes through anintersection. Simple heuristics based on the estimated traffic signalorientation and the average intersection width may be used to make anestimate as to these associations. These estimates may then be manuallyverified. This is particularly necessary for complex multi-laneintersections.

New labels may be continuously added and used to optimize theclassifier. Various optimization methods may be used, including gridsearch and hill climbing. For example, in a grid search, each axis ofthe parameter space may discretized, and all combinations of thesediscretized coordinates are evaluated. A coarse grid may provide insightinto the structure of the space to be optimized.

Although the steps of FIG. 2 are described as proceeding by use of asingle central server computer, images may be distributed across a setof computers such that each computer loads a small set of images intoRAM and then repeatedly evaluates the classifier according to thecurrent optimizer state on those cached images. A particular classifierconfiguration may be evaluated over a set, for example of 10,000 images,by a few hundred computers in under a second. This allows for the use ofiterative hill-climbing approaches, such as coordinate ascent, tooptimize the classifier in within a short period of time. Furtherparallelization may be possible by batching up all the states to beevaluated in a single optimizer step. If there are far more of theautomatically generated negative examples, the outcome of the positiveexamples may be weighed 10× higher.

Once the map information has been generated, the information may be usedto assist robotic vehicles or drivers. The traffic signal map may beused to identify and interpret traffic signals (red, yellow, or green).FIG. 4 depicts an exemplary flow diagram 400 of this process. As shownin block 410, the client device continuously determines the location ofthe client device with respect to a relevant portion of the trafficsignal map. For example, a vehicle-mounted or portable device may havedirect access to the traffic signal map or may request or automaticallyreceive portions of the map from the server based on the location of thedevice (or vehicle).

The client device then determines whether it is moving towards anintersection (or traffic signal), as shown in block 420. If the clientdevice is approaching an intersection, the client device may predict the3D location of traffic signals at the intersection based on the locationof the client device and the traffic signal map, as shown in block 430.

Using the vehicle pose and the traffic signal map, predictions may bemade about when traffic lights should be visible and where they shouldappear in the image frame. The position of the vehicle or device withrespect to the traffic light map may be estimated from one or more ofGPS, laser-based localization systems, inertial-aided GPS, camera-basedlocalization, or a lidar localization system which also identifieselevation. A kd tree or S2 spherical geometry cells and a simplevisibility model that includes the vehicle orientation as well as thetraffic signal orientation may be used to predict the position of nearbylights. The predicted positions may then be projected using the cameramodel into the image frame as an axis-aligned bounding box. The geometryof the bounding box may be determined based on the geometry of thetraffic signal as described by the map information. To account forinaccuracy in the prediction, in one example, the bounding box may bemade three times larger in each axis than the actual prediction.

The client device may use a camera to collect images and uses aclassifier to detect the red, yellow, or green blobs of these images atblocks 440 and 450.

The client device may use the classifier described above to findappropriately-sized brightly colored red, yellow, and green blobs withineach of the predicted bounding boxes. The geometry of the traffic lightsmay be used to distinguish between different types of lights. Forexample, if a particular traffic signal has a green light, theapproximate location, shape, and design of the traffic signal may beused to determine if the green light refers to a left arrow or a roundlight. The result is a set of possible classifications for each trafficsignal which can be used to identify the state of the traffic signal.

If there are no relevant blobs detected within the predicted locationsat block 460, the client device may assume some default state of thetraffic signal at block 480. The client device may assume a defaultstate such as a yellow light. Selecting yellow as a default may act as asafety mechanism as it indicates the need for the vehicle to slow down.It may be assumed that it is almost always safe to allow the vehicle toslow down when approaching an intersection in order to continue toclassify the state of the traffic signal. If there is no newclassification, the client device may determine that the state of thetraffic signal has not changed and that the light is still yellow.

In addition, the client device may also select the geometrically highestclassification within a predicted bounding box. For example, within awindow. For example, a given bounding box, there may be several lightsdetected. The client device may determine that the highest physicallight is corresponds to the traffic signal. This may prevent themisclassifying of objects such as orange pedestrian cross-walk lightsthat are frequently just below green lights, but well within thepredicted bounding box (see the “Don't Walk” sign 550 of FIG. 5).

Returning to FIG. 4 at block 470, if there are relevant blobs detectedby the classifier, the client device will determine whether there hassome been some state change to the traffic signal. If there is no statechange, the client device may again assume the default state of thetraffic signal at block 480. If there is some state change, the clientdevice may determine the type of change at block 490. Finally, the stateof the traffic signal has been determined and is ready to be used toassist the vehicle or user at block 495.

The state and position of the traffic signal may then be used to assista driver or robotic car. Where the device is used within a vehicle whichis driven by a person, the device may provide information regarding thestate of the traffic signal. For example, the device may provide visualor audible indications that the light is red, yellow or green, such as“the light is yellow.” In another example, the device may provideaudible instructions or warnings, such as “apply the brakes, the lightis red” or “the light is red, stop the vehicle.” In another example, thedevice may indicate that the correct light, such as a left turn signal,is not green. The device may be used to send instructions to the vehiclethat the brakes should be applied. If the device is used in conjunctionwith a navigation system, additional directions, such as “stop at thenext intersection, the light is red” may be included.

In the example of FIG. 5, the driver of vehicle 510 is approaching anintersection. The driver's view of traffic signal 540 may be limited orcompletely blocked by vehicle 520. The client device within vehicle 510may determine that the traffic signal is visible in the images taken bythe camera and may determine, for example, that traffic signal light545, a yellow light, is illuminated. The client device may pass thisinformation to the driver by, for example, using a visual or audiblesignal, stating “slow down, the light is yellow,” or indicating to thevehicle's computer the need to apply the brakes. If the client device isunable to discern the status of the traffic signal, the device may usethe default and again provide some visual or audible instruction to theuser. In another example, a vehicle may be stopped for red light of atraffic signal. When the light changes to green, the client device mayidentify the change in the state of the traffic signal and provide thisinformation to the driver or the vehicle's computer. After receiving theinformation, the driver or computer may, for example, begin toaccelerate the vehicle.

In a further example, the vehicle may be traveling on a particular routeand the client device may use its position to determine the lane oftravel through an intersection. Based on this information, the clientdevice may determine which traffic signals associated with theintersection are relevant to the intended route and update the vehicle'scomputer or the user on the status of the relevant traffic signals.

When a vehicle is operating autonomously, the device may decide aparticular path through an intersection by determining that within apredicted bounding box, no red or yellow lights are detected, and atleast one green light must be detected. It will be appreciated thatwhile there are generally multiple semantically identical lights in anintersection, it may only be necessary for the device to identify one ofthese lights to determine whether to pass through.

FIG. 6 is a distance histogram of predicted and detected lights duringan experimental 32 km drive along urban streets and highways duringwhich the device was used to control the car's passage throughintersections. In this example, traffic signals were detected out to 200m, which was when the device begins to predict that the traffic signalsshould be should be visible. The traffic signals are thus predicted evenwhere there are obstructions between the device in the vehicle and thetraffic signals (see FIG. 5 and associated description above).

FIG. 7 depicts the traffic signal confusion matrix for the experimentaldriving session. The matrix includes negative examples generated fromthe eight neighbors of each positive example. Yellow signal exampleswere not included in the ground truth.

Table 1 below contains true positives (tp), true negatives (tn), falsepositives (fp), and false negatives (fn) from the traffic signalconfusion matrix.

TABLE 1 Detector Positive Detector Negative Positive 856 (tp)  527 (fn)Negative  3 (fp) 6938 (tn)Base on Table 1, the experimental device's the precision was 99%(851/859=0.99), while the recall was 62% (851/1387=0.62). A latency ofapproximately 0.2 s was experienced primarily due to the latency for thetransmission of the image to the experimental device of approximately0.12 s. Based on camera bandwidth limitations, the frame rate of thedetection pipeline was 4 Hs. The processor load was less than 25% of asingle CPU (central processing unit), primarily due to the highresolution of the images. The experimental device worked best at nightand with moderate rain where the camera was mounted behind an area sweptby windshield wipers.

Although certain advantages are obtained when information is transmittedor received as noted above, aspects of the invention are not limited toany particular manner of transmission of information. For example, insome aspects, information may be sent via a medium such as an opticaldisk or portable drive. In other aspects, the information may betransmitted in a non-electronic format and manually entered into thesystem. Yet further, although some functions are indicated as takingplace on a server and others on a client, various aspects of the systemand method may be implemented by a single computer having a singleprocessor.

It will be further understood that the sample values, types andconfigurations of data described and shown in the figures are for thepurposes of illustration only. In that regard, systems and methods inaccordance with aspects of the invention may include different physicalattributes, data values, data types and configurations, and may beprovided and received at different times and by different entities(e.g., some values may be pre-suggested or provided from differentsources).

As these and other variations and combinations of the features discussedabove can be utilized without departing from the invention as defined bythe claims, the foregoing description of exemplary embodiments should betaken by way of illustration rather than by way of limitation of theinvention as defined by the claims. It will also be understood that theprovision of examples of the invention (as well as clauses phrased as“such as,” “e.g.”, “including” and the like) should not be interpretedas limiting the invention to the specific examples; rather, the examplesare intended to illustrate only some of many possible aspects.

Unless expressly stated to the contrary, every feature in a givenembodiment, alternative or example may be used in any other embodiment,alternative or example herein. For instance, any technology fordetermining the geographic location and orientation associated with acamera or particular image may be employed in any configuration herein.Each way of communicating or identifying the location of a trafficsignal or the status of the traffic signal may be used in anyconfiguration herein.

The invention claimed is:
 1. A method comprising: accessing a set ofimages which are associated with geographic locations proximate to atraffic intersection; for each particular image of the set of images,identifying, by one or more computing devices, brightly-colored red,yellow, and green objects within the particular image; generating, bythe one or more computers, a set of traffic signal labels based on theidentified brightly-colored red, yellow and green objects; filtering, bythe one or more computers, the set of traffic signal labels to removelabels for brightly-colored red, yellow, or green objects thatcorrespond to objects other than a traffic signal light; anddetermining, by the one or more computers, a three-dimensional locationof a traffic signal based on the filtered set of traffic signal labelsand the geographic locations of the images of the set of images.
 2. Themethod of claim 1, further comprising identifying associated labelswithin two or more images of the set of images, based on the geographiclocations of the two or more images of the set of images.
 3. The methodof claim 2, wherein determining the three-dimensional location of thetraffic signal is further based on the identified associated labels. 4.The method of claim 1, generating a map based on the three-dimensionallocation of the traffic signal.
 5. The method of claim 4, furthercomprising sending the map to a computer associated with a vehicle. 6.The method of claim 1, wherein the identified brightly colored red,yellow, and green objects are the size and aspect ratios to correspondto one or more traffic signals.
 7. The method of claim 1, whereinidentifying the associated labels is further based on an associationdistance between labeled objects of the set of images.
 8. The method ofclaim 1, wherein identifying the associated labels is further based onthe physical dimensions of one or more traffic signals.
 9. The method ofclaim 1, further comprising determining a lane associated with theparticular traffic signal based on comparing the determinedthree-dimensional location of the particular traffic signal to a map oflanes through the traffic intersection.
 10. A system comprising: memorystoring a set of images which are associated with geographic locationsproximate to a traffic intersection; one or more computers configuredto: for each particular image of the set of images, identifybrightly-colored red, yellow, and green objects within the particularimage; generate a set of traffic signal labels based on the identifiedbrightly-colored red, yellow and green objects; filter the set oftraffic signal labels to remove labels for brightly-colored red, yellow,or green objects that correspond to objects other than a traffic signallight; and determine a three-dimensional location of a traffic signalbased on the filtered set of traffic signal labels and the geographiclocations of the images of the set of images.
 11. The system of claim10, wherein the one or more computers are further configured to identifyassociated labels within two or more images of the set of images, basedon the geographic locations of the two or more images of the set ofimages.
 12. The system of claim 10, wherein the one or more computersare further configured to determine the three-dimensional location ofthe traffic signal further based on the identified associated labels.13. The system of claim 10, wherein the one or more computers arefurther configured to generate a map based on the three-dimensionallocation of the traffic signal.
 14. The system of claim 13, furthercomprising sending the map to a computer associated with a vehicle. 15.The system of claim 10, wherein the identified brightly colored red,yellow, and green objects are the size and aspect ratios to correspondto one or more traffic signals.
 16. The system of claim 10, whereinidentifying the associated labels is based on an association distancebetween labeled objects of the two or more selected images.
 17. Thesystem of claim 10, wherein identifying the associated labels is basedon the physical dimensions of a traffic signal.
 18. The system of claim10, the one or more computers are further configured to determine a laneassociated with the particular traffic signal based on comparing thedetermined three-dimensional location of the particular traffic signalto a map of lanes through the intersection.
 19. A non-transitorytangible computer-readable storage medium on which computer readableinstructions of a program are stored, the instructions, when executed bya processor, cause the processor to perform a method, the methodcomprising: accessing a set of images which are associated withgeographic locations proximate to a traffic intersection; for eachparticular image of the set of images, identifying brightly-colored red,yellow, and green objects within the particular image; generating a setof traffic signal labels based on the identified brightly-colored red,yellow and green objects; filtering the set of traffic signal labels toremove labels for brightly-colored red, yellow, or green objects thatcorrespond to objects other than a traffic signal light; and determininga three-dimensional location of a traffic signal based on the filteredset of traffic signal labels and the geographic locations of the imagesof the set of images.
 20. The medium of claim 19, wherein the methodfurther comprises identifying associated labels within two or moreimages of the set of images, based on the geographic locations of thetwo or more images of the set of images.